library(tidyverse)
library(Article3Package)

2% outlier and 2 % of uniform missing values

missing.prop <- 0.02

We know K

Gaussian data

K <- 6
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

We do not know K

Gaussian data

K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)
Hypothesis testing comparison

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)
Hypothesis testing comparison

2% outlier and 2 % of sparse missing values

missing.prop <- 0.02
missing.prop.by.loci <- 0.5

We know K

Gaussian data

K <- 6
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

We do not know K

Gaussian data

K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)
Hypothesis testing comparison

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)
Hypothesis testing comparison

20% outlier and 2 % of sparse missing values

missing.prop <- 0.02
missing.prop.by.loci <- 0.5
prop.outlier <- 0.2

We know K

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

Gaussian data

K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

20% outlier and 10 % of sparse missing values

missing.prop <- 0.1
missing.prop.by.loci <- 0.5
prop.outlier <- 0.2

We know K

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

Gaussian data

K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

20% outlier and 20 % of uniform missing values

missing.prop <- 0.20
prop.outlier <- 0.2

We know K

Gaussian data

K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

Logistic data

K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)
WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)
Hypothesis testing comparison

---
title: "LFMM with missing value"
author: "kevin caye"
date: "16 janvier 2017"
output: 
  html_notebook:
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
    theme: journal
    highlight: tango
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width = 10, fig.height = 13, 
                      message = FALSE, warning = FALSE,
                      results = "hide")
```

```{r}
library(tidyverse)
library(Article3Package)
```


# 2% outlier and 2 % of uniform missing values

```{r}
missing.prop <- 0.02
```


## We know K

### Gaussian data

```{r}
K <- 6
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```



## We do not know K

### Gaussian data

```{r}
K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)

```

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)

```






# 2% outlier and 2 % of sparse missing values

```{r}
missing.prop <- 0.02
missing.prop.by.loci <- 0.5
```


## We know K

### Gaussian data

```{r}
K <- 6
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```



## We do not know K

### Gaussian data

```{r}
K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = 0.02,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)

```

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = 0.02,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K + 1,
                 exp.rep = 3)

```

# 20% outlier and 2 % of sparse missing values

```{r}
missing.prop <- 0.02
missing.prop.by.loci <- 0.5
prop.outlier <- 0.2
```

## We know K

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```



### Gaussian data

```{r}
K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```



# 20% outlier and 10 % of sparse missing values

```{r}
missing.prop <- 0.1
missing.prop.by.loci <- 0.5
prop.outlier <- 0.2
```

## We know K

### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```


### Gaussian data

```{r}
K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  SparseMissingValueSampler(missing.prop = missing.prop, 
                            missing.prop.by.loci = missing.prop.by.loci)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```


# 20% outlier and 20 % of uniform missing values

```{r}
missing.prop <- 0.20
prop.outlier <- 0.2
```

## We know K

### Gaussian data

```{r}
K <- 3
## data
s <- NormalSampler(n = 100,
                   L = 1000,
                   K = K,
                   prop.outlier = prop.outlier,
                   sigma = 0.2,
                   c = 0.6,
                   mean.B = 0.0,
                   sd.B = 1.0,
                   sd.mu = 1.0,
                   mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```


### Logistic data

```{r}
K <- 3
## data
s <- LogisticSampler(n = 100,
                     ploidy = 2,
                     L = 1000,
                     K = K,
                     prop.outlier = prop.outlier,
                     sigma = 0.2,
                     c = 0.6,
                     mean.B = 0.0,
                     sd.B = 1.0,
                     sd.mu = 1.0,
                     mean.mu = 0.5) %>% 
  MissingValueSampler(missing.prop = missing.prop)

WithMissingValue(s,
                 method.K = K,
                 exp.rep = 3)

```

